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Post-traumatic stress disorder (PTSD) is a severe mental illness with grave social, political, economic, and humanitarian implications. To apply the principles of personalized omics-based medicine to this psychiatric problem, we implemented our previously introduced drug efficiency index (DEI) to the PTSD gene expression datasets. Generally, omics-based personalized medicine evaluates individual drug action using two classes of data: (1) gene expression, mutation, and Big Data profiles, and (2) molecular pathway graphs that reflect the protein–protein interaction. In the particular case of the DEI metric, we evaluate the drug action according to the drug’s ability to restore healthy (control) activation levels of molecular pathways. We have curated five PTSD and one TRD (treatment-resistant depression) cohorts of next-generation sequencing (NGS) and microarray hybridization (MH) gene expression profiles, which, in total, comprise 791 samples, including 379 cases and 413 controls. To check the applicability of our DEI metrics, we have performed three differential studies with gene expression and pathway activation data: (1) case samples vs. control samples, (2) case samples after treatment or/and observation vs. before treatment, and (3) samples from patients positively responding to the treatment vs. those responding negatively or non-responding patients. We found that the DEI values that use the signaling pathway impact activation (SPIA) metric were better than those that used the Oncobox pathway activation level (Oncobox PAL) approach. However, SPIA, Oncobox PAL, and DEI evaluations were reliable only if there were differential genes between case and control, or treated and untreated, samples.

The underlying molecular mechanisms of colorectal cancer (CRC) has attracted great attention from the scholarly community. The aim of our study is to identify pivotal genes related to the pathogenesis and prognosis of CRC. We integrated five microarray datasets from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were analyzed with the limma package. DAVID and OmicShare tools were used for Gene Ontology (GO) and KEGG enrichment analysis. The protein–protein interaction (PPI) network of DEGs was constructed. The prognostic analysis of hub genes was performed through Kaplan Meier-plotter. Finally, potential drugs were predicted in the CMap database. Through five microarray datasets, a total of 90 DEGs were detected including 54 up-regulated and 36 down-regulated genes. Biological process analysis showed DEGs were mainly enriched in positive regulation of neutrophil chemotaxis, chemokine-mediated signaling pathway, and bicarbonate transport. Signaling pathway analysis indicated that DEGs played a vital in proximal tubule bicarbonate reclamation, cell cycle and progesterone-mediated oocyte maturation. The GEPIA database confirmed that overexpression levels of hub genes were significantly associated with better survival of patients. Finally, the 20 most significant small molecules were obtained based on the CMap database. Our study has identified novel candidate biomarkers, pathways, and kinases associated with CRC prognosis.

Defeng Pan,1,* Yufei Zhou,2,* Shengjue Xiao,1,* Yue Hu,3,* Chunyan Huan,1 Qi Wu,1 Xiaotong Wang,1 Qinyuan Pan,1 Jie Liu,1 Hong Zhu1 1Department of Cardiology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, 221004, People’s Republic of China; 2Department of Cardiology, Shanghai Institute of Cardiovascular Diseases, Zhongshan Hospital and Institutes of Biomedical Sciences, Fudan University, Shanghai, 200032, People’s Republic of China; 3Department of General Practice, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, 221004, People’s Republic of China*These authors contributed equally to this workCorrespondence: Defeng PanDepartment of Cardiology, The Affiliated Hospital of Xuzhou Medical University, 99 Huaihai West Road, Xuzhou, 221004, Jiangsu, People’s Republic of ChinaTel +86 516-83262017Email xzdefengpan@xzhmu.edu.cnIntroduction: Atrial fibrillation (AF) is the most prevalent sustained cardiac arrhythmia, but the molecular mechanisms underlying AF are not known. We aimed to identify the pivotal genes and pathways involved in AF pathogenesis because they could become potential biomarkers and therapeutic targets of AF.Methods: The microarray datasets of GSE31821 and GSE41177 were downloaded from the Gene Expression Omnibus database. After combining the two datasets, differentially expressed genes (DEGs) were screened by the Limma package. MicroRNAs (miRNAs) confirmed experimentally to have an interaction with AF were screened through the miRTarBase database. Target genes of miRNAs were predicted using the miRNet database, and the intersection between DEGs and target genes of miRNAs, which were defined as common genes (CGs), were analyzed. Functional and pathway-enrichment analyses of DEGs and CGs were performed using the databases DAVID and KOBAS. Protein–protein interaction (PPI) network, miRNA- messenger(m) RNA network, and drug-gene network was visualized. Finally, reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR) was used to validate the expression of hub genes in the miRNA–mRNA network.Results: Thirty-three CGs were acquired from the intersection of 65 DEGs from the integrated dataset and 9777 target genes of miRNAs. Fifteen “hub” genes were selected from the PPI network, and the miRNA-mRNA network, including 82 miRNAs and 9 target mRNAs, was constructed. Furthermore, with the validation by RT-qPCR, macrophage migration inhibitory factor (MIF), MYC proto-oncogene, bHLH transcription factor (MYC), inhibitor of differentiation 1 (ID1), and C-X-C Motif Chemokine Receptor 4 (CXCR4) were upregulated and superoxide Dismutase 2 (SOD2) was downregulated in patients with AF compared with healthy controls. We also found MIF, MYC, and ID1 were enriched in the transforming growth factor (TGF)-β and Hippo signaling pathway.Conclusion: We identified several pivotal genes and pathways involved in AF pathogenesis. MIF, MYC, and ID1 might participate in AF progression through the TGF-β and Hippo signaling pathways. Our study provided new insights into the mechanisms of action of AF.Keywords: atrial fibrillation, bioinformatics analysis, differentially expressed genes, pathways, protein–protein interaction network

Yang Guo,1– 4 Bobin Ning,5 Qunhui Zhang,1– 4 Jing Ma,4 Linlin Zhao,1– 4 QiQin Lu,1– 4 Dejun Zhang1,4 1Research Center for High Altitude Medicine, Medical College of Qinghai University, Xining, 810001, People’s Republic of China; 2Key Laboratory of Application and Foundation for High Altitude Medicine Research in Qinghai Province, Medical College of Qinghai University, Xining, 810001, People’s Republic of China; 3Qinghai-Utah Joint Research Key Lab for High Altitude Medicine, Medical College of Qinghai University, Xining, 810001, People’s Republic of China; 4Department of Eco-Environmental Engineering, Qinghai University, Xining, 810016, People’s Republic of China; 5Department of Medicine, The General Hospital of the People’s Liberation Army, Beijing, 100038, People’s Republic of ChinaCorrespondence: Dejun ZhangDepartment of Eco-Environmental Engineering, Qinghai University, Qinghai, 810016, People’s Republic of ChinaEmail djzhangqhu@163.comPurpose: The objective of this study was to identify the potential regulatory mechanisms, diagnostic biomarkers, and therapeutic drugs for heart failure (HF).Methods: Differentially expressed genes (DEGs) between HF and non-failing donors were screened from the GSE57345, GSE5406, and GSE3586 datasets. Database for Annotation Visualization and Integrated Discovery and Metascape were used for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses respectively. The GSE57345 dataset was used for weighted gene co-expression network analysis (WGCNA). The intersecting hub genes from the DEGs and WGCNA were identified and verified with the GSE5406 and GSE3586 datasets. The diagnostic value of the hub genes was calculated through receiver operating characteristic analysis and net reclassification index (NRI). Gene set enrichment analysis (GSEA) was used to filter out the signaling pathways associated with the hub genes. SYBYL 2.1 was used for molecular docking of hub targets and potential HF drugs obtained from the connection map.Results: Functional annotation of the DEGs showed enrichment of negative regulation of angiogenesis, endoplasmic reticulum stress response, and heart development. PTN, LUM, ISLR, and ASPN were identified as the hub genes of HF. GSEA showed that the key genes were related to the transforming growth factor-β (TGF-β) and Wnt signaling pathways. Sirolimus, LY-294002, and wortmannin have been confirmed as potential drugs for HF.Conclusion: We identified new hub genes and candidate therapeutic drugs for HF, which are potential diagnostic, therapeutic and prognostic targets and warrant further investigation.Keywords: differentially expressed genes, weighted gene co-expression network analysis, diagnostic biomarkers, therapeutic drugs, heart failure

Background: Papillary thyroid carcinoma (PTC) is one of the most common endocrine malignancies and has a favorable prognosis. However, optimal treatments and prognostic markers have not been clearly identified. Methods: Gene expression data from primary PTC were downloaded from the Gene Expression Omnibus database and subjected to two analyses of differentially expressed genes (DEGs), followed by intersecting individual and integrated DEGs analyses as well as gene set enrichment analysis. Analysis of data from Sequence Read Archive and The Cancer Genome Atlas, immunohistochemistry and qRT-PCR of TFF3 were performed to validate the results. Finally, the relationship between gene expression and disease-free survival as well as immune cell infiltration were investigated. Results: Six critical DEGs and several tumor-enriched signaling pathways were identified. Immunohistochemistry and qRT-PCR validated the low expression of TFF3 in PTC. TFF3 and FCGBP are coexpressed in PTC, and patients with lower gene expression had worse disease-free survival but higher immune cell infiltration. Conclusion: TFF3 was significantly underexpressed and may function with FCGBP synergistically in PTC.

Vaccines can be powerful tools, but for some diseases, safe and effective vaccines have been elusive. New developments in nucleic acid sequencing, bioinformatics, and protein modeling are facilitating the discovery of previously unknown antigens through reverse vaccinology approaches. Sequencing the complementarity- determining region of antibodies and T cell receptors allows detailed assessment of the immune repertoire and identification of paratopes shared by many individuals, supporting the selection of antigens that may be broadly protective. Systems vaccinology approaches to asses the global host response to vaccination by evaluation of differentially expressed genes in blood, cellular or tissue transcriptomes can reveal previously unknown pathways and interactions related to protective immunity. While it is important to remember that discoveries made through reverse vaccinology and systems vaccinology must still be confirmed with traditional challenge models and clinical trials, these approaches can provide new perspectives that may help solve longstanding problems in veterinary vaccinology.

Background We aim to discover some prognostic factors, provide a basis for discovering molecular markers, and provide a basis for molecular features of early lung adenocarcinoma (LUAD) to predict patient prognosis. Methods Sequence data of LUAD were downloaded from The Cancer Genome Atlas (TCGA) database to screen out differentially expressed lncRNAs, miRNAs, and mRNAs (DERs). DERs were identified using R software’s limma package. The competitive endogenous RNA (ceRNA) network was constructed based on these RNAs. Univariate and multivariate Cox regression analysis on the RNAs in the ceRNA screened out independent prognostic-related RNAs to construct a prognostic risk score (PS) model. Combined with clinical data, we can calculate the survival rate of patients with early LUAD. Results There were 2,701 differentially expressed mRNAs (DEmRNAs), 47 differentially expressed lncRNAs (DElncRNAs), and 161 differentially expressed miRNAs (DEmiRNAs) identified in early LUAD. Based on these RNAs, 32 lncRNAs, 87 miRNAs, and 174 mRNAs participated in the ceRNA network. Twelve independently prognostic-related RNAs form an optimized combination. We developed a PS model based on these RNAs. Age, tumor recurrence and PS model status were independent survival prognostic clinical factors. Nomogram was established to predict the 3-year and 5-year survival rates. Conclusions We successfully constructed a ceRNA regulatory network based on the DERs in early LUAD. It can help us clarify the molecular mechanism of early LUAD. Simultaneously, the prognostic-related RNAs in early LUAD were also screened out. This network could provide new bases for diagnoses and prognoses of patients with LUAD.

Background: Intracranial aneurysm (IA) is a cerebrovascular disease that seriously endangers human heath and life. However, the pathogenesis of IA has not been clarified. Objective: In this study, ...

Purpose Nasopharyngeal carcinoma (NPC) is one of the most common malignant tumors of the head and neck. This study aimed to investigate the crucial genes and regulatory networks involved in the carcinogenesis of NPC using a bioinformatics approach. Methods Five mRNA and two miRNA expression datasets were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) and miRNAs (DEMs) between NPC and normal samples were analyzed using R software. The WebGestalt tool was used for functional enrichment analysis, and protein-protein interaction (PPI) network analysis of DEGs was performed using STRING database. Transcription factors (TFs) were predicted using TRRUST and Transcriptional Regulatory Element Database (TRED). Kinases were identified using X2Kgui. The miRNAs of DEGs were predicted using miRWalk database. A kinase-TF-mRNA-miRNA integrated network was constructed, and hub nodes were selected. The hub genes were validated using NPC datasets from the GEO and Oncomine databases. Finally, candidate small-molecule agents were predicted using CMap. Results A total of 122 DEGs and 44 DEMs were identified. DEGs were associated with the immune response, leukocyte activation, endoplasmic reticulum stress in GO analysis, and the NF-κB signaling pathway in KEGG analysis. Four significant modules were identified using PPI network analysis. Subsequently, 26 TFs, 73 kinases, and 2499 miRNAs were predicted. The predicted miRNAs were cross-referenced with DEMs, and seven overlapping miRNAs were selected. In the kinase-TF-mRNA-miRNA integrated network, eight genes (PTGS2, FN1, MMP1, PLAU, MMP3, CD19, BMP2, and PIGR) were identified as hub genes. Hub genes were validated with consistent results, indicating the reliability of our findings. Finally, six candidate small-molecule agents (phenoxybenzamine, luteolin, thioguanosine, reserpine, blebbistatin, and camptothecin) were predicted. Conclusion We identified DEGs and an NPC regulatory network involving kinases, TFs, mRNAs, and miRNAs, which might provide promising insight into the pathogenesis, treatment, and prognosis of NPC.

Background Microbes threaten human health in space exploration. Studies have shown that Proteus mirabilis has been found in human space habitats. In addition, the biological characteristics of P. mirabilis in space have been studied unconditionally. The simulated microgravity environment provides a platform for understanding the changes in the biological characteristics of P. mirabilis. Objective This study intends to explore the effect of simulated microgravity on P. mirabilis, the formation of P. mirabilis biofilm, and its related mechanism. Methods The strange deformable rods were cultured continuously for 14 days under microgravity simulated in high-aspect rotating vessels (HARVs). The morphology, growth rate, metabolism, and biofilm formation of the strain were measured, and the phenotypic changes of P. mirabilis were evaluated. Transcriptome sequencing was used to detect differentially expressed genes under simulated microgravity and compared with phenotype. Results The growth rate, metabolic ability, and biofilm forming ability of P. mirabilis were lower than those of normal gravity culture under the condition of simulated microgravity. Further analysis showed that the decrease of growth rate, metabolic ability, and biofilm forming ability may be caused by the downregulation of related genes (pstS, sodB, and fumC). Conclusion The simulated microgravity condition enables us to explore the potential relationship between bacterial phenotype and molecular biology, thus opening up a suitable and constructive method for medical fields that have not been explored before. It provides a certain strategy for the treatment of P. mirabilis infectious diseases in space environment by exploring the microgravity of P. mirabilis.

Xiangui Meng,1– 3,* Hongwei Yuan,1– 3,* Weiquan Li,1– 3,* Wen Xiao,1– 3 Xiaoping Zhang1– 3 1Department of Urology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, People’s Republic of China; 2Shenzhen Huazhong University of Science and Technology Research Institute, Shenzhen, 518000, People’s Republic of China; 3Institute of Urology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xiaoping Zhang; Wen XiaoDepartment of Urology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Avenue, Wuhan, Hubei Province, 430022, People’s Republic of ChinaTel +86-18602752025Fax +86 2785776343Email xzhang@hust.edu.cn; xiaowenx11@163.comBackground: Studies report that conventional treatment of clear cell renal cell carcinoma (ccRCC) is effective, but several advanced patients present with poor prognosis. The current study explored potential new tumor markers and therapeutic targets in advanced ccRCC.Methods: Biomarker gene expression of ccRCC was retrieved from GEO database and the Cancer Genome Atlas Kidney Clear Cell Carcinoma (TCGA-KIRC) database. Gene ontology (GO) analysis and protein–protein interaction (PPI) networks of biomarker genes were constructed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) tool. Kaplan–Meier analysis and receiver operating characteristic curve (ROC) analysis were performed to explore the prognostic and diagnostic roles of these genes. Gene set enrichment analysis (GSEA) analysis was used to determine hallmark functions of the biomarker genes. qRT-PCR was used to verify the reliability of the analysis results in tumor tissues.Results: A total of 21 upregulated genes were identified between advanced ccRCC and early ccRCC (grade III+IV vs grade I+II). Gene ontology analysis showed that the 21 upregulated genes were mainly implicated in biological processes including metabolic and lipid transport. The findings showed that 7 out of the 21 genes were significantly upregulated in 72-paired samples retrieved from the TCGA-KIRC. High expression of 5 genes indicated a poor prognosis of overall survival and disease-free survival in KIRC. Three genes effectively distinguished renal cancer tissue and adjacent renal tissues in a total of 533 ccRCC samples. GSEA showed that the 3 biomarkers were significantly enriched in epithelial–mesenchymal transition, G2M checkpoint, and angiogenesis. The results of qRT-PCR showed that STEAP3, IBSP, and AQP9 had a significant identification effect in ccRCC.Conclusion: The findings showed that 3 biomarkers were significantly upregulated in advanced ccRCC and could be used for diagnosis, prediction, and potential novel therapeutic targets for progression of ccRCC.Keywords: advanced ccRCC, biomarker, progression, differentially expressed genes

The development and progression of colorectal cancer (CRC) are known to be affected by the interplay between tumor and immune cells. However, the impact of CRC cells on the systemic immunity has yet to be elucidated. We aimed to comprehensively evaluate the circulating immune subsets and transcriptional profiles of CRC patients. In contrast to healthy controls (HCs), CRC patients had a lower percentage of B and T lymphocytes, T helper (Th) cells, non-classical monocytes, dendritic cells, and a higher proportion of polymorphonuclear myeloid-derived suppressor cells, as well as a reduced expression of CD69 on NK cells. Therefore, CRC patients exhibit a more evident systemic immune suppression than HCs. A diagnostic model integrating seven immune subsets was constructed to distinguish CRC patients from HCs with an AUC of 1.000. Moreover, NR3C2, CAMK4, and TRAT1 were identified as candidate genes regulating the number of Th cells in CRC patients. The altered composition of circulating immune cells in CRC could complement the regional immune status of the tumor microenvironment and contribute to the discovery of immune-related biomarkers for the diagnosis of CRC.

Triple-negative breast cancer (TNBC) is more difficult to treat and has a higher mortality rate than other subtypes. Although hormone receptor-targeted therapy is an effective treatment to increase survival rate in breast cancer patients, it is not suitable for TNBC patients. To address the issues, differentially expressed genes (DEGs) in TNBC patients from the Gene Expression Omnibus (GEO) database were analyzed. A total of 170 genes were obtained from three Genomic Spatial Events (GSEs) using the intersection of each GSE dataset and 61 DEGs were identified after validation with the gene enrichment analysis. We combined this with the degree scores from the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and protein-protein interaction (PPI) network, of which 7 genes were correlated with survival rate. Finally, a proteomics database revealed that only the CHK1 protein level was differently expressed in basal-like compared with other subtypes. We demonstrated that CHK1 expression was higher in TNBC cell lines compared with non-TNBC cell lines, and CHK1 promotes epithelial to mesenchymal transition (EMT) as well as migration and invasion ability. Our study provides new insight into the TNBC subnetwork that may be useful in the prognosis and treatment of TNBC patients.

Cyanide (CN−) pollution in agricultural systems can affect crop production. However, no data are available to describe the full picture of the responsive metabolic mechanisms of genes with known functions related to exogenous KCN exposure. In this study, we examined the transcriptome in rice seedlings exposed to potassium cyanide (KCN) using an Agilent 4×44K rice microarray to clarify the relationship between the differentially expressed genes (DEGs) and their function classifications. The number of DEGs (up-regulated genes/down-regulated genes) was 322/626 and 640/948 in the shoots and roots of CN−-treated rice seedlings, respectively. Functional predication demonstrated that a total of 534 and 837 DEGs in shoots and roots were assigned to 22 COG categories. Four common categories listed on the top five COG classifications were detected in both rice tissues: signal transduction mechanisms, carbohydrate transport and metabolism, post-translational modification, protein turnover and chaperones, and transcription. A comparison of DEGs aligned to the same COG classification demonstrated that the majority of up-regulated/down-regulated DEGs in rice tissues were significantly different, suggesting that responsive and regulatory mechanisms are tissue specific in CN−-treated rice seedlings. Additionally, fifteen DEGs were aligned to three different COG categories, implying their possible multiple functions in response to KCN stress. The results presented here provide insights into the novel responsive and regulatory mechanisms of KCN-responsive genes, and will serve as useful resources for further functional dissections of the physiological significance of specific genes activated in the exogenous KCN stress response in rice plants.

Cancer stem cells (CSC) play one of the crucial roles in the pathogenesis of various cancers, including colorectal cancer (CRC). Although great efforts have been made regarding our understanding of the cancerogenesis of CRC, CSC involvement in CRC development is still poorly understood. Using bioinformatics and RNA-seq data of normal mucosa, colorectal adenoma, and carcinoma (n = 106) from GEO and TCGA, we identified candidate CSC genes and analyzed pathway enrichment analysis (PEI) and protein–protein interaction analysis (PPI). Identified CSC-related genes were validated using qPCR and tissue samples from 47 patients with adenoma, adenoma with early carcinoma, and carcinoma without and with lymph node metastasis and were compared to normal mucosa. Six CSC-related genes were identified: ANLN, CDK1, ECT2, PDGFD, TNC, and TNXB. ANLN, CDK1, ECT2, and TNC were differentially expressed between adenoma and adenoma with early carcinoma. TNC was differentially expressed in CRC without lymph node metastases whereas ANLN, CDK1, and PDGFD were differentially expressed in CRC with lymph node metastases compared to normal mucosa. ANLN and PDGFD were differentially expressed between carcinoma without and with lymph node metastasis. Our study identified and validated CSC-related genes that might be involved in early stages of CRC development (ANLN, CDK1, ECT2, TNC) and in development of metastasis (ANLN, PDGFD).

Background: The mechanisms underlying myocardial ischemia/reperfusion (I/R) injury are not fully understood. This study aims to explore key candidate genes and potential therapeutic targets for treatment of myocardial I/R injury.Methods: The transcriptional profiles of ventricular myocardium during cardiac arrest, ischemia, and reperfusion were obtained from the Gene Expression Omnibus database. Based on the transcriptional data of GSE6381, functional pathway and process enrichment analyses, protein–protein interaction network, and gene set enrichment analyses were conducted. In the animal experiments, we established the myocardial I/R injury model in mice. We validated the mRNA and protein expression of the key genes using the qPCR and western blots. We further assessed the expression and localization of CCL21 and its receptors using immunofluorescence staining experiments.Results: The microarray analyses identified five key genes (CCL21, XCR1, CXCL13, EDN1, and CASR). Myocardial I/R process in mice resulted in significant myocardial infraction, histological damage, and myocardial apoptosis. The results of qPCR and western blots showed that the expression of CCL21 and CXCL13 were increased following myocardial I/R injury in mice. Furthermore, the immunofluorescence staining results revealed that the expression of GPR174/CCR7 (CCL21 receptors), but not CXCR5 (CXCL13 receptor), was elevated following myocardial I/R injury. Moreover, the activated CCL21-GPR174/CCR7 signaling was located on the cardiac fibroblasts of the myocardium with I/R injury.Conclusion: This study revealed several key factors underlying myocardial I/R injury. Of these, the activation of CCL21-GPR174/CCR7 signaling on cardiac fibroblasts was highlighted, which provides potential therapeutic targets for cardioprotection.

Since the molecular mechanisms underlying in the pathogenesis of cardiovascular diseases (CVD) are extremely complex and have not yet been elucidated in detail, CVD remain the leading cause of death worldwide. Traditional Chinese medicine involves the treatment of disease from an overall perspective, and its therapeutic effects on CVD have been demonstrated. However, the mechanisms contributing to the multiscale treatment of cardiovascular diseases at the systematic level remain unclear. Network pharmacology methods and a gene chip data analysis were integrated and applied in the present study, which was conducted to investigate the potential target genes and related pathways of Shenfu Decoction (SFD) for the treatment of myocardial injury. The gene chip analysis was initially performed, followed by network pharmacology to identify differentially expressed genes (DEG) and a functional enrichment analysis. Protein-protein networks were constructed and a module analysis was conducted. A network analysis was used to identify the target genes of SFD. Regarding the results obtained, 1134 DEG were identified using the STRING website. The module analysis revealed that nine hub genes exhibited ubiquitin-protein ligase activity. Therefore, SFD significantly alters the expression of ubiquitination-related genes and, thus, plays an important therapeutic role in the treatment of heart failure. In conclusion, hub genes may provide a more detailed understanding of the molecular mechanisms of action of as well as candidate targets for SFD therapy.

Xin Shang, Lan-Er Shi, Dina Taule, Zhang-Zhi Zhu The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, People’s Republic of ChinaCorrespondence: Zhang-Zhi ZhuThe First Affiliated Hospital of Guangzhou University of Chinese Medicine, No. 12, Jichang Road, Guangzhou, Guangdong, 510405, People’s Republic of ChinaTel +86-18620120683Fax +86- 20-36591595Email zzz@gzucm.edu.cnBackground: Currently, there is still a lack of understanding about the mechanism and therapeutic targets of pancreatic adenocarcinoma (PAAD). The potential of miRNA–mRNA networks for the identification of regulatory mechanisms involved in PAAD development remains unexplored.Methods: We compared differentially expressed miRNAs (DEMIs) and differentially expressed genes (DEGs) in PAAD and normal tissues from the Gene Expression Omnibus (GEO) database. Transcription factors (TFs) were obtained from FunRich. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of DEGs and DEMIs were implemented using Database for Annotation, Visualization and Integrated Discovery (DAVID). Then, key miRNAs and targeted mRNAs were identified by assessment of their expression and prognosis in UALCAN and Kaplan–Meier plotters. In the last step, the candidate miRNA–mRNA selected was confirmed by real-time quantitative polymerase chain reaction (qRT-PCR).Results: We distinguished 62 significant DEMIs, 1314 upregulated DEGs, and 1110 downregulated DEGs. The top 10 TFs were identified. In total, there were 160 hub genes obtained by intersecting the set of 2224 predicted targets with the set of significant DEGs. And we selected 8 key miRNAs. Furthermore, low expression of miR-455-3p in PAAD tissue was closely connected with poor prognosis, and only 5 target mRNAs were predicted to be increased in PAAD tissue with poor prognosis. Therefore, a novel miRNA–hub gene regulatory network in PAAD was constructed. Finally, in vitro experiments indicated that miR-455-3p expression was decreased in PAAD sample. HOXC4, DLG4, DYNLL1 and FBXO45 were validated by qRT-PCR as highly probable targets of miR-455-3p.Conclusion: A novel miRNA–mRNA axis has been discovered that may be involved in the regulation of transcriptional disorders and affected the survival of PAAD patients, which would provide a novel strategy for the treatment of PAAD.Keywords: pancreatic adenocarcinoma, bioinformatics analysis, differentially expressed genes, differentially expressed microRNAs, hsa-miR-455-3p

Stroke is a sudden cerebrovascular circulatory disorder with high morbidity, disability, mortality, and recurrence rate, but its pathogenesis and key genes are still unclear. In this study, bioinformatics was used to deeply analyze the pathogenesis of stroke and related key genes, so as to study the potential pathogenesis of stroke and provide guidance for clinical treatment. Gene Expression profiles of GSE58294 and GSE16561 were obtained from Gene Expression Omnibus (GEO), the differentially expressed genes (DEGs) were identified between IS and normal control group. The different expression genes (DEGs) between IS and normal control group were screened with the GEO2R online tool. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the DEGs were performed. Using the Database for Annotation, Visualization and Integrated Discovery (DAVID) and gene set enrichment analysis (GSEA), the function and pathway enrichment analysis of DEGS were performed. Then, a protein–protein interaction (PPI) network was constructed via the Search Tool for the Retrieval of Interacting Genes (STRING) database. Cytoscape with CytoHubba were used to identify the hub genes. Finally, NetworkAnalyst was used to construct the targeted microRNAs (miRNAs) of the hub genes. A total of 85 DEGs were screened out in this study, including 65 upward genes and 20 downward genes. In addition, 3 KEGG pathways, cytokine − cytokine receptor interaction, hematopoietic cell lineage, B cell receptor signaling pathway, were significantly enriched using a database for labeling, visualization, and synthetic discovery. In combination with the results of the PPI network and CytoHubba, 10 hub genes including CEACAM8, CD19, MMP9, ARG1, CKAP4, CCR7, MGAM, CD79A, CD79B, and CLEC4D were selected. Combined with DEG-miRNAs visualization, 5 miRNAs, including hsa-mir-146a-5p, hsa-mir-7-5p, hsa-mir-335-5p, and hsa-mir-27a- 3p, were predicted as possibly the key miRNAs. Our findings will contribute to identification of potential biomarkers and novel strategies for the treatment of ischemic stroke, and provide a new strategy for clinical therapy.

Methods for testing the equality of two means are of critical importance in many areas of applied statistics. In the microarray context, it is often necessary to apply this kind of testing to small samples containing no more than a dozen elements, when inevitably the power of these tests is low. We suggest an augmentation of the classical t-test by introducing a new test statistic which we call "bio-weight." We show by simulation that in practically important cases of small sample size, the test based on this statistic is substantially more powerful than that of the classical t-test. The power computations are accompanied by ROC and FDR analysis of the simulated microarray data.

Affymetrix high-density oligonucleotide microarray makes it pos- sible to simultaneously measure, and thus compare the expression profiles of hundreds of thousands of genes in living cells. Genes differentially ex- pressed in different conditions are very important to both basic and medical research. However, before detecting these differentially expressed genes from a vast number of candidates, it is necessary to normalize the microarray data due to the significant variation caused by non-biological factors. During the last few years, normalization methods based on probe level or probeset level intensities were proposed in the literature. These methods were motivated by different purposes. In this paper, we propose a multivariate normaliza- tion method, based on partial least squares regression, aiming to equalize the central tendency, reduce and equalize the variation of the probe level intensities in any probeset across the replicated arrays. By so doing, we hope that one can precisely estimate the gene expression indexes.

Differently expressed genes of atherosclerotic sample analysis are helpful to sort the prominent genes that influence the plaque formation and progression. Scientific evidence-based protein-protein interaction network (PPIN) studies were used to find hub proteins in complex disease conditions. Druggable capacity is one of the important parameters to confirm as a successful drug target. Construction of protein interaction network and principal node analysis (PNA) on atherosclerotic data sets lead to screen the hub proteins. Furthermore, druggable property of protein pocket confirms the targetability of susceptible target candidates and for target selection. Differentially expressed genes are identified through GEO2R analyzer on data sets of various atherosclerotic samples. STRING database and Cytoscape are employed to construct PPIN. Targets were identified by PNA such as centrality measures and clustering algorithm. Gene Ontology enrichment also used as one of the screening parameters to filter the candidates related to atherosclerotic terms. Topological evaluation of target protein was successfully done by ITASSER and GROMACS, respectively. Grid-based principle of DoGSiteScorer is utilized for druggability analysis. Six proteins such as integrin alpha L (ITGAL), metallothionein 1F (MT1F), metallothionein 1X (MT1X), P-selectin glycoprotein ligand-1 (SELPLG), solute carrier family 30 A, zinc transporter protein (SLC30A1), and TNFSF13B are screened as potential biomarkers through network-based analysis. Among the six, ITGAL, SELPLG, SLC30A1, and TNSF13B are identified as better prioritized atherosclerotic targets through druggability efficiency.

Since the imbalance of gene expression has been demonstrated to tightly related to breast cancer (BRCA) genesis and growth, common genes expressed of BRCA were screened to explore the essence in-between. In current work, most common differentially expressed genes (DEGs) in various subtypes of BRCA were identified. Functional enrichment analysis illustrated the driving factor of deactivation of the cell cycle and the oocyte meiosis, which critically triggers the development of BRCA. Herein, we constructed a 12-gene prognostic risk model relative to differential gene expression. Subsequently, the K-M curves, analysis on time-ROC curve and Cox regression were performed to assess this risk model by determining the respective prognostic value, and the prediction performance were ascertained for both training and validation cohorts. In addition, multivariate Cox regression was analysed to reveal the independence between risk score and prognostic stage, and the accuracy and sensitivity of prognosis are particularly improved after clinical indicators are included into the analysis. In summary, this study offers novel insights into the imbalance of gene expression within BRCA, and highlights 12 selected genes associated with patient prognosis. The risk model can help individualize treatment for patients at different risks, and propose precise strategies and treatments for BRCA therapy.

This study aimed to identify long non-coding RNAs (lncRNAs) involving in the skeletal muscle aging process. Skeletal muscle samples from old and young subjects were collected for lncRNA-sequencing. Differentially expressed genes (DEGs) and DElncRNAs between young and old groups were identified and a co-expression network was built. Further, a dexamethasone-induced muscle atrophy cell model was established to characterize the function of a critical lncRNA. A total of 424 DEGs, including 271 upregulated genes and 153 downregulated genes as well as 152 DElncRNAs including 76 up-regulated and 76 down-regulated lncRNAs were obtained. Functional analysis demonstrated that the DEGs were significantly related to immune response. Coexpression network demonstrated lncRNA AC004797.1, PRKG1-AS1 and GRPC5D-AS1 were crucial lncRNAs. Their expressions were further validated by qRT-PCR in human skeletal muscle and the muscle atrophy cell model. Further in vitro analysis suggested that knock-down of PRKG1-AS1 could significantly increase cell viability and decrease cell apoptosis. qRT-PCR and western blot analyses demonstrated that knock-down of PRKG1-AS1 could increase the expression of MyoD, MyoG and Mef2c. This study demonstrated that lncRNAs of GPRC5D-AS1, AC004797.1 and PRKG1-AS1 might involve the aging-associated disease processes.

Hepatitis C virus-associated HCC (HCV-HCC) is a prevalent malignancy worldwide and the molecular mechanisms are still elusive. Here, we screened 240 differentially expressed genes (DEGs) of HCV-HCC from Gene expression omnibus (GEO) and the Cancer Genome Atlas (TCGA), followed by weighted gene coexpression network analysis (WGCNA) to identify the most significant module correlated with the overall survival. 10 hub genes (CCNB1, AURKA, TOP2A, NEK2, CENPF, NUF2, CDKN3, PRC1, ASPM, RACGAP1) were identified by four approaches (Protein-protein interaction networks of the DEGs and of the significant module by WGCNA, and diagnostic and prognostic values), and their abnormal expressions, diagnostic values, and prognostic values were successfully verified. A four hub gene-based prognostic signature was built using the least absolute shrinkage and selection operator (LASSO) algorithm and a multivariate Cox regression model with the ICGC-LIRI-JP cohort (N =112). Kaplan-Meier survival plots (P = 0.0003) and Receiver Operating Characteristic curves (ROC = 0.778) demonstrated the excellent predictive potential for the prognosis of HCV-HCC. Additionally, upstream regulators including transcription factors and miRNAs of hub genes were predicted, and candidate drugs or herbs were identified. These findings provide a firm basis for the exploration of the molecular mechanism and further clinical biomarkers development of HCV-HCC.

As an important source of vegetable oil, peanut (Arachis hypogea L.) with both adequate tolerance and high-oleate could be a profitable choice in saline-alkali soil. In our study, two high-oleic peanut lines derived from the same cross and differing in saline-alkali response were initially identified from our previous field evaluation. In this study they were subjected to saline-alkali stress in pot culture, and their transcriptomes were analyzed. 16L1 possessed multiple characteristics related to saline-alkali stress tolerant, including well-developed green leaves, more pods, elevated proline, high levels of superoxide dismutase, catalase, peroxidase and strong photosynthetic capacity. According to the correlation of various indicators, we have established high-oleic peanut biological saline-alkali tolerance and physiological saline-alkali evaluation system. Ten differentially expressed genes (DEGs) responding to saline-alkali stress were characterized and confirmed by quantitative realtime reverse transcription PCR. A series of analyses of DEGs revealed the importance of secondary metabolites, osmotic adjustment substances, plant hormone signal transduction, and protein kinase in peanut saline-alkali tolerance. The output of this study will help to understand the saline-alkali tolerant mechanism and provide a reference for peanut saline-alkali tolerance.

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal type of cancer. In this study, we undertook a pairwise comparison of gene expression pattern between tumor tissue and its matching adjacent normal tissue for 45 PDAC patients and identified 22 upregulated and 32 downregulated genes. PPI network revealed that fibronectin 1 and serpin peptidase inhibitor B5 were the most interconnected upregulated-nodes. Virtual screening identified bleomycin exhibited reasonably strong binding to both proteins. Effect of bleomycin on cell viability was examined against two PDAC cell lines, AsPC-1 and MIA PaCa-2. AsPC-1 did not respond to bleomycin, however, MIA PaCa-2 responded to bleomycin with an IC50 of 2.6 μM. This implicates that bleomycin could be repurposed for the treatment of PDAC, especially in combination with other chemotherapy agents. In vivo mouse xenograft studies and patient clinical trials are warranted to understand the functional mechanism of bleomycin towards PDAC and optimize its therapeutic efficacy. Furthermore, we will evaluate the antitumor activity of the other identified drugs in our future studies.

Lentil cultivation could be challenged by combined heat and drought stress in semi-arid regions. We used RNA-seq approach to profile transcriptome changes of Lens culinaris exposed to individual and combined heat and drought stresses. It was determined that most of the differentially expressed genes observed in response to combined stress, could not be identified by analysis of transcriptome exposed to corresponding individual stresses. Interestingly, this study results revealed that the expression of ribosome generation and protein biosynthesis and starch degradation pathways related genes were uniquely up-regulated under the combined stress. Although multiple genes related to antioxidant activity were up-regulated in response to all stresses, variation in types and expression levels of these genes under the combined stress were higher than that of individual stresses. Using this comparative approach, for the first time, we reported up-regulation of several TF, CDPK, CYP, and antioxidant genes in response to combined stress in plants.

Objective: Cervical cancer (CC) ranks fourth most diagnosed cancer and cancer mortality in women. Long non-coding RNAs (lncRNAs) take important roles in CC development. This study aimed to identify more and novel competing endogenous RNA (ceRNA) mechanisms of lncRNAs in CC. Materials and methods: The miRNA expression dataset GSE20592 and lncRNA/mRNA expression dataset GSE63514 were downloaded from Gene Expression Omnibus. The differentially expressed genes (DEGs), differentially expressed lncRNAs (DElncRNAs), and differentially expressed miRNAs (DEmiRNAs) between CC tumor and normal samples were identified with the criteria of adj.P.Value 2. Functional enrichment analysis was performed for DEGs. The interaction pairs among lncRNAs, miRNAs and mRNAs were predicted and the ceRNA network was then constructed. Survival analysis was performed based on the TCGA dataset. Results: Totally, 42 DEmiRNAs, 25 DElncRNAs, and 518 DEGs were identified in CC tumor samples versus normal tissues. The DEGs were associated with ‘GO:0006260: DNA replication’, ‘GO:0051301: cell division’, and ‘hsa01100:Metabolic pathways’. The ceRNA network consisted of 878 lncRNA-miRNA-mRNA pairs. Of the miRNAs, lncRNAs, and genes with the top 10 interaction degrees in the ceRNA network, the upregulated cyclin dependent kinase inhibitor 2A gene (CDKN2A) was targeted by the downregulated DEmiRNAs including hsa-miR-125b-5p and hsa-miR-125a-5p, which were targeted by the upregulated DElncRNA BBOX1-AS1. The high expression level of CDKN2A contributed to the poor overall survival of patients with CC. Conclusions: The BBOX1-AS1-hsa-miR-125b-5p/hsa-miR-125a-5p-CDKN2A ceRNA network is of great value in CC development.

Studies on hereditary fixation of the tame-behavior phenotype during animal domestication remain relevant and important because they are of both basic research and applied significance. In model animals, gray rats Rattus norvegicus bred for either an enhancement or reduction in defensive response to humans, for the first time, we used high-throughput RNA sequencing to investigate differential expression of genes in tissue samples from the tegmental region of the midbrain in 2-month-old rats showing either tame or aggressive behavior. A total of 42 differentially expressed genes (DEGs; adjusted p-value  <  0.01 and fold-change  >  2) were identified, with 20 upregulated and 22 downregulated genes in the tissue samples from tame rats compared with aggressive rats. Among them, three genes encoding transcription factors (TFs) were detected: Ascl3 was upregulated, whereas Fos and Fosb were downregulated in tissue samples from the brains of tame rats brain. Other DEGs were annotated as associated with extracellular matrix components, transporter proteins, the neurotransmitter system, signaling molecules, and immune system proteins. We believe that these DEGs encode proteins that constitute a multifactorial system determining the behavior for which the rats have been artificially selected. We demonstrated that several structural subtypes of E-box motifs—known as binding sites for many developmental TFs of the bHLH class, including the ASCL subfamily of TFs—are enriched in the set of promoters of the DEGs downregulated in the tissue samples of tame rats’. Because ASCL3 may act as a repressor on target genes of other developmental TFs of the bHLH class, we hypothesize that the expression of TF gene Ascl3 in tame rats indicates longer neurogenesis (as compared to aggressive rats), which is a sign of neoteny and domestication. Thus, our domestication model shows a new function of TF ASCL3: it may play the most important role in behavioral changes in animals.

Deoxynivalenol (DON) is a mycotoxin that is found in feed ingredients derived from grains such as corn and wheat. Consumption of DON-contaminated feed has been shown to cause damage to the intestine, kidneys, and liver. However, the molecular mechanism by which DON exerts its effect in the small intestine is not completely understood. As a result, we profiled gene expression in intestinal epithelial cells treated with DON and examined the molecular function in vitro. We hypothesized that DON could induce apoptosis via the FOXO3a-signaling pathway in intestinal epithelial cells based on these findings. DON induced the apoptosis and the translocation of FOXO3a into the nucleus. Moreover, the inhibiting of FOXO3a alleviated the apoptosis and expression of apoptosis-related genes (TRAL, BCL-6, CASP8, and CASP3). ERK1/2 inhibitor treatment suppressed the translocation of FOXO3a into the nucleus. Our discovery suggests that DON induces apoptosis in intestinal epithelial cells through the FOXO3a-signaling pathway.

Xanthomonas oryzae pv. oryzae (Xoo) is a destructive pathogen that causes bacterial blight disease of rice worldwide. Xoo uses T3SS (type III secretion system) effectors to subvert rice innate immunity. However, the comprehensive knowledge of rice genes involved in T3SS effectors-mediated interaction remains unclear. In this study, the transcriptome profiles of rice infected with a virulent Xoo strain from North-eastern region of India relatives to its avirulent strain (that lacks functional T3SS) were analyzed at early (2–6 hpi) and late (16–24 hpi) hours of infection. Out of total 255 differentially expressed genes (DEGs), during early infection, 62 and 70 genes were upregulated and downregulated, respectively. At late infection, 70 and 53 genes were upregulated and downregulated, respectively. The transcriptomic data identified many differentially expressed resistant genes, transposons, transcription factors, serine/threonine protein kinase, cytochrome P450 and peroxidase genes that are involved in plant defense. Pathway analysis revealed that these DEGs are involved in hormone signaling, plant defense, cellular metabolism, growth and development processes. DEGs associated with plant defense were also validated through quantitative real-time PCR. Our study brings a comprehensive picture of the rice genes that are being differentially expressed during bacterial blight infection. Nevertheless, the DEG-associated pathways would provide sensible targets for developing resistance to bacterial blight. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13205-022-03193-4.

Liver diseaseis perhaps the deadliest malignant growth on the planet. In momentum contemplates, the capabilities for being chosen as key qualities in illnesses is bit low, constraining the precision of the anticipated key qualities in infections. To distinguish the key qualities of liver malignant growth with high exactness, and coordinated different microarray quality articulation datasets identified with the liver disease utilized. At that point recognize their basic DEGs (Differentially Expressed Genes) which will bring about more exact than those from the individual dataset. The datasets are on the whole human microarray quality articulation information recovered from the GEO (Gene Expression Omnibus) database and need to discover differentially communicated qualities among wellbeing and liver malignancy conditions. In light of these qualities, a protein-protein association system can be built and dissected to recognize the qualities tests that are having a higher impact on the system. These quality examples are prepared by utilizing a neural system LSTM. From this prepared neural system, the key hubs can be recognized and they can be considered as the key qualities of liver malignant growth. In addition, the strategy can be applied to different sorts of informational collections to choose key qualities of other complex ailments.

Background. Hypoxia closely relates to malignant progression and appears to be prognostic for outcome in hepatocellular carcinoma (HCC). Our research is aimed at mining the hypoxic-related genes (HRGs) and constructing a prognostic predictor (PP) model on clinical prognosis in HCC patients. Methods. RNA-sequencing data about HRGs and clinical data of patients with HCC were obtained from The Cancer Genome Atlas (TCGA) database portal. Differentially expressed HRGs between HCC and para-carcinoma tissue samples were obtained by applying the Wilcox analysis in R statistical software. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes were used for gene functional enrichment analyses. Then, the patients who were asked to follow up for at least one month were enrolled in the following study. Cox proportional risk regression model was applied to obtain key HRGs which related to overall survival (OS) in HCC. PP was constructed and defined, and the accuracy of PP was validated by constructing the signature in a training set and validation set. Connectivity map (CMap) was used to find potential drugs, and gene set cancer analysis (GSCA) was also performed to explore the underlying molecular mechanisms. Results. Thirty-seven differentially expressed HRGs were obtained. It contained 28 upregulated and 9 downregulated genes. After the univariate Cox regression model analysis, we obtained 27 prognosis-related HRGs. Of these, 25 genes were risk factors for cancer, and 2 genes were protective factors. The PP was composed by 12 key genes (HDLBP, SAP30, PFKP, DPYSL4, SLC2A1, HMOX1, PGK1, ERO1A, LDHA, ENO2, SLC6A6, and TPI1). GSCA results showed the overall activity of these 12 key genes in 10 cancer-related pathways. Besides, CMap identified deferoxamine, crotamiton, talampicillin, and lycorine might have effects with HCC. Conclusions. This study firstly reported 12 prognostic HRGs and constructed the model of the PP. This comprehensive research of multiple databases helps us gain insight into the biological properties of HCC and provides deferoxamine, crotamiton, talampicillin, and lycorine as potential drugs to fight against HCC.

The purpose of the study was to provide the pipeline for processing of publicly available unprocessed data on gene expression via integration and differential gene expression analysis. Data collection from open gene expression databases, normalization and integration into a single expression matrix in accordance with metadata and determination of differentially expressed genes were fulfilled. To demonstrate all stages of data processing and integrative analysis, there were used the data from gene expression in the human placenta from the first and second trimesters of normal pregnancy. The source code for the integrative analysis was written in the R programming language and publicly available as a repository on GitHub. Four clusters of functionally enriched differentially expressed genes were identified for the human placenta in the interval between the first and second trimester of pregnancy. Immune processes, developmental processes, vasculogenesis and angiogenesis, signaling and the processes associated with zinc ions varied in the considered interval between the first and second trimester of placental development. The proposed sequence of actions for integrative analysis could be applied to any data obtained by microarray technology.

Skin color is colorful for birds, which has been reported to be associated with multi-biological functions, such as crypsis, camouflage, social signaling and mate choice, but little is known about its underlying molecular mechanism. Studies on the major genes affecting the black skin color of ducks. For this purpose, Silver ammonia staining and RNA-seq analysis were carried out to identify the differences in tissue morphology and gene expressions between black and yellow skin ducks. The silver ammonia dyes slice results showed that in the development of black duck, the content of melanin in black skin gradually increased and then decreased, and the content of melanin in yellow and black skin was significantly different. Through transcriptome, a total of 102 and 84 differentially expressed genes (DEGs) were identified in beak skin and web skin, respectively. These DEGs were enriched in melanin biosynthesis and play a critical role in melanogenesis pathway. Co-expression analysis showed that EDNRB2 was the only gene associated with black skin color in DEGs, which was also consistent with qRT-PCR. The melanin synthesis pathway dominated by EDNRB2 up-regulated the amount of melanin synthesis, leading to the formation of black skin in ducks.

Objective: An impervious mortality rate in oral cancer (OC) to a certain extent explains the exigencies of precise biomarkers. Therefore, the study was intended to identify OC candidate biomarkers using samples of healthy normal tissues (N=335), adjacent normal tissues (N=93) and OC tissues (N=533) from online microarray data. Methods: Differentially expressed genes (DEGs) were recognised through GeneSpring software (Fold change >4.0 and ‘p’ value

Motivation Data generated from high-throughput technologies such as sequencing, microarray and bead-chip technologies are unavoidably affected by batch effects (BEs). Large effort has been put into developing methods for correcting these effects. Often, BE correction and hypothesis testing cannot be done with one single model, but are done successively with separate models in data analysis pipelines. This potentially leads to biased P-values or false discovery rates due to the influence of BE correction on the data. Results We present a novel approach for estimating null distributions of test statistics in data analysis pipelines where BE correction is followed by linear model analysis. The approach is based on generating simulated datasets by random rotation and thereby retains the dependence structure of genes adequately. This allows estimating null distributions of dependent test statistics, and thus the calculation of resampling-based P-values and false-discovery rates following BE correction while maintaining the alpha level. Availability The described methods are implemented as randRotation package on Bioconductor: https://bioconductor.org/packages/randRotation/ Supplementary information Supplementary data are available at Bioinformatics online.

The molecular resistance mechanism of Aspergillus oryzae (A. oryzae) under arsenate (As(V)) stress was investigated by comparative transcriptome analysis. In total, 4911 differentially expressed ge...

Gummosis (Phytophthora root and crown rot) is an important and destructive disease that affects fruit trees like pistachio (Pistacia vera L.). Although many studies have been performed on the possi...

ObjectiveJuvenile idiopathic arthritis (JIA) is the most common type of arthritis among children, but a few studies have investigated the contribution of rare variants to JIA. In this study, we aimed to identify rare coding variants associated with JIA for the genome-wide landscape.MethodsWe established a rare variant calling and filtering pipeline and performed rare coding variant and gene-based association analyses on three RNA-seq datasets composed of 228 JIA patients in the Gene Expression Omnibus against different sets of controls, and further conducted replication in our whole-exome sequencing (WES) data of 56 JIA patients. Then we conducted differential gene expression analysis and assessed the impact of recurrent functional coding variants on gene expression and signalling pathway.ResultsBy the RNA-seq data, we identified variants in two genes reported in literature as JIA causal variants, as well as additional 63 recurrent rare coding variants seen only in JIA patients. Among the 44 recurrent rare variants found in polyarticular patients, 10 were replicated by our WES of patients with the same JIA subtype. Several genes with recurrent functional rare coding variants have also common variants associated with autoimmune diseases. We observed immune pathways enriched for the genes with rare coding variants and differentially expressed genes.ConclusionThis study elucidated a novel landscape of recurrent rare coding variants in JIA patients and uncovered significant associations with JIA at the gene pathway level. The convergence of common variants and rare variants for autoimmune diseases is also highlighted in this study.

Objective: Prostate cancer (PCa) is the most frequent cancer found in males worldwide, and its mortality rate is increasing every year. To discover key molecular changes in PCa development and metastasis, we analyzed microarray data of localized PCa, metastatic PCa and normal prostate tissue samples from clinical specimens. Methods: Gene expression profiling datasets of PCa were analyzed online. The DAVID was used to perform GO functional and KEGG pathway enrichment analyses. CytoHubba in Cytoscape software was applied to identify hub genes. Survival data were downloaded from GEPIA. Gene expression data were obtained from ONCOMINE and UALCAN. Results: We obtained 4 sets of differentially expressed genes (DEGs), DEGs 1: a comparison of the gene expression between 4 normal prostate and 5 localized PCa samples in GSE27616; DEGs 2: a comparison of the gene expression between 6 normal prostate and 7 localized PCa samples in GSE3325; DEGs 3: a comparison of the gene expression between 5 localized PCa and 4 metastatic PCa samples in GSE27616; DEGs 4: a comparison of the gene expression between 7 localized PCa and 6 metastatic PCa samples in GSE3325. A comparison of these 4 sets of genes revealed 51 overlapped genes. GO function analysis revealed enrichment of the 51 DEGs in functions related to the proteinaceous extracellular matrix and centrosome, protein homodimerization activity and chromatin binding were the main functions of these genes, which participated in regulating cell division, mitotic nuclear division, proteinaceous extracellular matrix, cell adhesion and apoptotic process. KEGG pathway analysis indicated that these identified DEGs were mainly enriched in progesterone-mediated oocyte maturation, oocyte meiosis and cell cycle. We defined the 16 genes with the highest degree of connectivity as the hub genes in the 51 overlapped DEGs. Cox regression revealed TOP2A, CCNB2, BUB1, CDK1 and EZH2 were related to Disease-free survival (DFS). The expression levels of the 5 genes were 2.232-, 1.786-, 2.303-, 1.699-, and 1.986-fold higher in PCa than the levels in normal tissues, respectively (P < 0.05). We obtained 20 hub genes from DEGs by the comparison of normal prostate tissue vs. localized cancer tissue. Among them, KIF20A, CDKN3, PBK and CDCA2, were expressed higher in PCa than in normal tissues, and were associated with the DFS of PCa patients. Meanwhile, we obtained 20 hub genes from DEGs by the comparison of localized cancer tissue vs. metastatic cancer tissue. Cox regression revealed PLK1, CCNA2 and CDC20, were associated with both the DFS and overall survival of PCa patients. Conclusions: The results suggested that the functions of KIF20A, CDKN3, PBK and CDCA2 may contribute to PCa development and the functions of PLK1, CCNA2 and CDC20 may contribute to PCa metastasis. Meanwhile, the functions of TOP2A, CCNB2, BUB1, CDK1 and EZH2 may contribute to both PCa development and metastasis.